





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_lowerCAmelCase								=  logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")

_lowerCAmelCase								=  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_lowerCAmelCase								=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def      _lowerCAmelCase		(	_lowerCAmelCase  ):
   '''simple docstring'''


   with open(__snake_case	,"""rb"""  ) as f:
      A_  :					List[str]         =						Image.open(__snake_case  )
      return im.convert("""RGB"""  )

@dataclass
class  _UpperCAmelCase				:
      a    							=				field(
          default=a_      ,							metadata={
              '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
          }      ,							)
      a    							=				field(
          default=a_      ,							metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
      a    							=				field(default=a_      ,							metadata={'''help''': '''A folder containing the training data.'''}  )
      a    							=				field(default=a_      ,							metadata={'''help''': '''A folder containing the validation data.'''}  )
      a    							=				field(
          default=0.15      ,							metadata={'''help''': '''Percent to split off of train for validation.'''}  )
      a    							=				field(
          default=a_      ,							metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                  '''value if set.'''
              )
          }      ,							)
      a    							=				field(
          default=a_      ,							metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                  '''value if set.'''
              )
          }      ,							)
      def 		_lowerCamelCase   (		self							):

         if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
            raise ValueError(
                """You must specify either a dataset name from the hub or a train and/or validation directory."""							)






@dataclass
class  _UpperCAmelCase				:
      a    							=				field(
          default='''google/vit-base-patch16-224-in21k'''      ,							metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      ,							)
      a    							=				field(
          default=a_      ,							metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(a_  )}      ,							)
      a    							=				field(
          default=a_      ,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
      a    							=				field(
          default=a_      ,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}  )
      a    							=				field(
          default='''main'''      ,							metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}      ,							)
      a    							=				field(default=a_      ,							metadata={'''help''': '''Name or path of preprocessor config.'''}  )
      a    							=				field(
          default=a_      ,							metadata={
              '''help''': (
                  '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                  '''with private models).'''
              )
          }      ,							)
      a    							=				field(
          default=a_      ,							metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}      ,							)






def      _lowerCAmelCase		(	_lowerCAmelCase  ):
   '''simple docstring'''


   A_  :					List[str]         =						torch.stack([example["""pixel_values"""] for example in examples]  )
   A_  :					List[str]         =						torch.tensor([example["""labels"""] for example in examples]  )
   return {"pixel_values": pixel_values, "labels": labels}



def      _lowerCAmelCase		(	):
   '''simple docstring'''


   A_  :					Any         =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
   if len(sys.argv  ) == 2 and sys.argv[1].endswith(""".json"""  ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      A_   ,					A_   ,					A_  :					Union[str, Any]         =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
   else:
      A_   ,					A_   ,					A_  :					List[Any]         =						parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry("""run_image_classification"""	,__snake_case	,__snake_case  )

   # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,datefmt="""%m/%d/%Y %H:%M:%S"""	,handlers=[logging.StreamHandler(sys.stdout  )]	,)

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   A_  :					Dict         =						training_args.get_process_log_level()
   logger.setLevel(__snake_case  )
   transformers.utils.logging.set_verbosity(__snake_case  )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # Log on each process the small summary:
   logger.warning(
       f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
       + f"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
   logger.info(f"""Training/evaluation parameters {training_args}"""  )

   # Detecting last checkpoint.
   A_  :					Union[str, Any]         =						None
   if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
      A_  :					Optional[int]         =						get_last_checkpoint(training_args.output_dir  )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
         raise ValueError(
             f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             """Use --overwrite_output_dir to overcome."""  )
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""  )

    # Set seed before initializing model.
   set_seed(training_args.seed  )

   # Initialize our dataset and prepare it for the 'image-classification' task.
   if data_args.dataset_name is not None:
      A_  :					Optional[int]         =						load_dataset(
          data_args.dataset_name	,data_args.dataset_config_name	,cache_dir=model_args.cache_dir	,task="""image-classification"""	,use_auth_token=True if model_args.use_auth_token else None	,)
   else:
      A_  :					Tuple         =						{}
      if data_args.train_dir is not None:
         A_  :					str         =						os.path.join(data_args.train_dir	,"""**"""  )
      if data_args.validation_dir is not None:
         A_  :					str         =						os.path.join(data_args.validation_dir	,"""**"""  )
      A_  :					str         =						load_dataset(
          """imagefolder"""	,data_files=__snake_case	,cache_dir=model_args.cache_dir	,task="""image-classification"""	,)

   # If we don't have a validation split, split off a percentage of train as validation.
   A_  :					Optional[Any]         =						None if """validation""" in dataset.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split	,__snake_case  ) and data_args.train_val_split > 0.0:
      A_  :					List[Any]         =						dataset["""train"""].train_test_split(data_args.train_val_split  )
      A_  :					Any         =						split["""train"""]
      A_  :					Optional[int]         =						split["""test"""]

   # Prepare label mappings.
   # We'll include these in the model's config to get human readable labels in the Inference API.
   A_  :					List[Any]         =						dataset["""train"""].features["""labels"""].names
   A_   ,					A_  :					List[Any]         =						{}, {}
   for i, label in enumerate(__snake_case  ):
      A_  :					List[Any]         =						str(__snake_case  )
      A_  :					int         =						label

   # Load the accuracy metric from the datasets package
   A_  :					str         =						evaluate.load("""accuracy"""  )

   # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
   # predictions and label_ids field) and has to return a dictionary string to float.
   def compute_metrics(_lowerCAmelCase  ):
      return metric.compute(predictions=np.argmax(p.predictions	,axis=1  )	,references=p.label_ids  )

   A_  :					Optional[int]         =						AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path	,num_labels=len(__snake_case  )	,labelaid=__snake_case	,idalabel=__snake_case	,finetuning_task="""image-classification"""	,cache_dir=model_args.cache_dir	,revision=model_args.model_revision	,use_auth_token=True if model_args.use_auth_token else None	,)
   A_  :					Tuple         =						AutoModelForImageClassification.from_pretrained(
       model_args.model_name_or_path	,from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )	,config=__snake_case	,cache_dir=model_args.cache_dir	,revision=model_args.model_revision	,use_auth_token=True if model_args.use_auth_token else None	,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,)
   A_  :					List[str]         =						AutoImageProcessor.from_pretrained(
       model_args.image_processor_name or model_args.model_name_or_path	,cache_dir=model_args.cache_dir	,revision=model_args.model_revision	,use_auth_token=True if model_args.use_auth_token else None	,)

   # Define torchvision transforms to be applied to each image.
   if "shortest_edge" in image_processor.size:
      A_  :					List[Any]         =						image_processor.size["""shortest_edge"""]
   else:
      A_  :					List[Any]         =						(image_processor.size["""height"""], image_processor.size["""width"""])
   A_  :					str         =						Normalize(mean=image_processor.image_mean	,std=image_processor.image_std  )
   A_  :					Optional[Any]         =						Compose(
       [
           RandomResizedCrop(__snake_case  ),
           RandomHorizontalFlip(),
           ToTensor(),
           normalize,
       ]  )
   A_  :					Optional[Any]         =						Compose(
       [
           Resize(__snake_case  ),
           CenterCrop(__snake_case  ),
           ToTensor(),
           normalize,
       ]  )

   def train_transforms(_lowerCAmelCase  ):
      A_  :					Tuple         =						[
          _train_transforms(pil_img.convert("""RGB"""  )  ) for pil_img in example_batch["""image"""]
      ]
      return example_batch

   def val_transforms(_lowerCAmelCase  ):
      A_  :					Optional[int]         =						[_val_transforms(pil_img.convert("""RGB"""  )  ) for pil_img in example_batch["""image"""]]
      return example_batch

   if training_args.do_train:
      if "train" not in dataset:
         raise ValueError("""--do_train requires a train dataset"""  )
      if data_args.max_train_samples is not None:
         A_  :					Any         =						(
             dataset["""train"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
         )
      # Set the training transforms
      dataset["train"].set_transform(__snake_case  )

   if training_args.do_eval:
      if "validation" not in dataset:
         raise ValueError("""--do_eval requires a validation dataset"""  )
      if data_args.max_eval_samples is not None:
         A_  :					Any         =						(
             dataset["""validation"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
         )
      # Set the validation transforms
      dataset["validation"].set_transform(__snake_case  )

   # Initalize our trainer
   A_  :					int         =						Trainer(
       model=__snake_case	,args=__snake_case	,train_dataset=dataset["""train"""] if training_args.do_train else None	,eval_dataset=dataset["""validation"""] if training_args.do_eval else None	,compute_metrics=__snake_case	,tokenizer=__snake_case	,data_collator=__snake_case	,)

   # Training
   if training_args.do_train:
      A_  :					Optional[int]         =						None
      if training_args.resume_from_checkpoint is not None:
         A_  :					Optional[int]         =						training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         A_  :					List[str]         =						last_checkpoint
      A_  :					Dict         =						trainer.train(resume_from_checkpoint=__snake_case  )
      trainer.save_model()
      trainer.log_metrics("""train"""	,train_result.metrics  )
      trainer.save_metrics("""train"""	,train_result.metrics  )
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      A_  :					Tuple         =						trainer.evaluate()
      trainer.log_metrics("""eval"""	,__snake_case  )
      trainer.save_metrics("""eval"""	,__snake_case  )

   # Write model card and (optionally) push to hub
   A_  :					Union[str, Any]         =						{
       """finetuned_from""": model_args.model_name_or_path,
       """tasks""": """image-classification""",
       """dataset""": data_args.dataset_name,
       """tags""": ["""image-classification""", """vision"""],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**__snake_case  )
   else:
      trainer.create_model_card(**__snake_case  )


if __name__ == "__main__":
      main()














"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


snake_case : List[Any]     		=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case : List[Any]     		=		{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : Optional[int]     		=		{
    """unc-nlp/lxmert-base-uncased""": 5_1_2,
}

snake_case : List[Any]     		=		{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_INIT_CONFIGURATION
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = LxmertTokenizer







    def __init__(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[str]=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	Optional[int]=True				,    UpperCamelCase_ :	Dict="[UNK]"				,    UpperCamelCase_ :	str="[SEP]"				,    UpperCamelCase_ :	Dict="[PAD]"				,    UpperCamelCase_ :	Optional[int]="[CLS]"				,    UpperCamelCase_ :	Any="[MASK]"				,    UpperCamelCase_ :	Any=True				,    UpperCamelCase_ :	List[Any]=None				,    **UpperCamelCase_ :	Dict				,    ):





           '''simple docstring'''

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    do_lower_case=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    tokenize_chinese_chars=UpperCamelCase_				,    strip_accents=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
           if (
               normalizer_state.get('lowercase'				,    UpperCamelCase_ ) != do_lower_case
               or normalizer_state.get('strip_accents'				,    UpperCamelCase_ ) != strip_accents
               or normalizer_state.get('handle_chinese_chars'				,    UpperCamelCase_ ) != tokenize_chinese_chars
           ):
                  __magic_name__      				=    getattr(UpperCamelCase_				,    normalizer_state.pop('type' ) )
                  __magic_name__      				=    do_lower_case
                  __magic_name__      				=    strip_accents
                  __magic_name__      				=    tokenize_chinese_chars
                  __magic_name__      				=    normalizer_class(**UpperCamelCase_ )

           __magic_name__      				=    do_lower_case







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Optional[int]=None ):





           '''simple docstring'''

           __magic_name__      				=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output







    def 				a__			(		self :	int				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           __magic_name__      				=    self._tokenizer.model.save(UpperCamelCase_				,    name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )